Peer-to-Peer Trading for Energy-Saving Based on Reinforcement Learning

Liangyi Pu, Song Wang, Xiaodong Huang, Xing Liu, Yawei Shi and Huiwei Wang ()
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Liangyi Pu: Chongqing Huizhi Energy Corporation Ltd., State Power Investment Corporation (SPIC), Chongqing 401127, China
Song Wang: Chongqing Huizhi Energy Corporation Ltd., State Power Investment Corporation (SPIC), Chongqing 401127, China
Xiaodong Huang: Chongqing Huizhi Energy Corporation Ltd., State Power Investment Corporation (SPIC), Chongqing 401127, China
Xing Liu: College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
Yawei Shi: College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
Huiwei Wang: Key Laboratory of Intelligent Information Processing, Chongqing Three Gorges University, Chongqing 404100, China

Energies, 2022, vol. 15, issue 24, 1-16

Abstract: This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community’s welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community.

Keywords: peer-to-peer energy trading; multi-agent reinforcement learning; prosumer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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